Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain

behalf of COVID-19 SEMICYUC Working Group, on; Rodríguez, Alejandro; Ruíz-Botella, Manuel; Martín‐Loeches, Ignacio; Herrera, María; Solé‐Violán, Jordi; Gómez, Josep; Bodi, María; Trefler, Sandra; Papiol, Elisabeth; Dı́az, Emili; Suberviola, Borja; Vallverdú, Montserrat; Mayor-Vázquez, Eric; Moreno, Antonio; Canabal Berlanga, Alfonso; Sánchez, Miguel; del Valle Ortiz, María; Ballesteros, Juan; Martín Iglesias, Lorena; Marín‐Corral, Judith; López Ramos, Esther; Hidalgo Valverde, Virginia; Vidaur Tello, Loreto; Sancho Chinesta, Susana; Javier Gonzáles de Molina, Francisco; Herrero García, Sandra; Carolina Sena Pérez, Carmen; Carlos Pozo Laderas, Juan; Rodríguez-García, Raquel; Estella, Ángel; Ferrer, Ricard

doi:10.1186/s13054-021-03487-8

Public

Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain

Published in Critical Care • Feb 15, 2021

Authors:

on behalf of COVID-19 SEMICYUC Working Group

Alejandro Rodríguez

Manuel Ruíz-Botella

Abstract

BACKGROUND: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. METHODS: Prospective, multicenter, observational s...

Subject

Medicine

Critically ill

Coronavirus disease 2019 (COVID-19)

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